My research interest mainly lies in large scale inference and distributed computing. In my previous work with Dr. Junwei Lu, we propose an inferential framework testing the general community combinatorial properties in Stochastic Block Models (SBM), which is not well explored previously due to its complex nature. We present a general framework applicable to all symmetric community properties and show in theory a combinatorial-probabilistic trade-off phenomenon. In my another work supervised by Dr. Xihong Lin, we develop a FAst DIstributed (FADI) PCA method that uses multiple random sketches to perform principal component analysis on federated data, which greatly improves the computational efficiency while maintaining the same error rate as the traditional PCA methods.